Insurance Objection Response LLM for Rate Change Inquiries
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Solution Overview
Problem
Conventional large language models and generative AI tools are ineffective in addressing the manual and time-consuming process of responding to insurance rate change requests, which involve multiple rounds of objections and inquiries from different state departments, requiring significant human effort from actuarial analysts and managers.
Innovation Solution
A customized large language model system using artificial intelligence to parse and generate responses to objection inquiry documents for insurance rate change requests, leveraging historical data and natural language processing to automate the response generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional large language models are used for generating responses to objections, then the process remains manual and time-consuming, but the quality and accuracy of responses deteriorates due to insufficient customization
Solution Approach 1:
The patent customizes the large language model by training it on domain-specific data from insurance rate change requests, objections, and historical responses. This parameter change involves adjusting the model's knowledge base and response patterns to match the specific requirements of insurance regulatory communications, thereby improving response quality while maintaining automation.
Solution Approach 2:
The system creates a customized version of the large language model by copying and adapting general AI capabilities to the specific domain of insurance rate change objections. The model learns from historical data patterns and replicates successful response structures, ensuring both automated operation and high-quality, domain-appropriate responses.
2Reliability
If manual processes are used to respond to objections, then response quality can be maintained through human review, but the time required and human resources consumed increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training the large language model on historical data before actual response generation is needed. The model learns from past objections and responses, preparing its internal parameters to quickly generate accurate responses to new objections without requiring time-consuming manual analysis during the actual response generation process.
Solution Approach 2:
The customized large language model performs self-service by autonomously generating responses to objections without requiring continuous human intervention. The model independently analyzes objection documents, retrieves relevant information from its training data, and produces responses that maintain quality standards previously achieved through manual review.
3Measurement precision
If a customized large language model is trained on historical data, then response accuracy improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The system extracts and isolates the essential training data from the larger corpus of historical insurance documents. By selectively extracting relevant patterns, objection types, and response structures from historical data, the system achieves high accuracy without needing to process every document, thereby reducing the practical complexity of data handling.
Solution Approach 2:
The patent introduces an intermediary data processing layer that bridges the raw historical data and the final model training. This intermediary layer processes and cleanses the data, extracting only the relevant information needed for training, which simplifies the overall system architecture by separating data preparation from the core modeling functions.
Data Source
AI summary
A computer system may be provided. The computer system may be programmed to (i) build the large language model for insurance rate change requests; (ii) receive a current objection inquiry document for a rate change request from an insurance regulator; (iii) electronically parse the current objection inquiry document to identify a first model input including text describing the at least one first objection and the at least one first request for additional information; (iv) enter the first model input into the large language model to generate a first output including an electronic response document for responding to the current objection inquiry for the rate change request; and (v) transmit the electronic response document to the insurance regulator to respond to the at least one first objection and the at least one first request for additional information included in the current objection inquiry document.